Rapid improvements in fast information storage capacity, high computing
power, and parallelization have contributed to the swift uptake of the
deep learning technology in end-use industries such as automotive and
healthcare. Further, the need for understanding and analyzing visual
contents among enterprises in order to gain meaningful insights, is
expected to provide traction to the industry over the forecast period.

The increasing prominence of Graphics Processing Unit (GPU)-accelerated
applications is leading to increased adoption of the technology in
scientific disciplines such as deep learning and data science.
Organizations are utilizing deep learning neural networks to extract
valuable insights from enormous amounts of data for providing innovative
products and improving customer experience, thereby increasing revenue
opportunities.

The technology is expected to gain prominence among key players and
researchers, owing to its use in improvising artificial intelligence
capabilities in Natural Language Processing (NLP), image & speech
recognition, and computer vision areas. Solution providers are resorting
to partnerships and collaborations to enter the deep learning space.